Consumer and business owner rights and legal access: An HCI based approach

Abstract

Nowadays, people are being cheated every day while buying groceries. The sad thing is that even if people take measures to avoid awareness, the results are revealed very late. Another issue is that people still do not fully know what laws apply and what steps should be taken if they are deceived while buying a product. And because of this, the syndicate system in the market is increasing day by day. The main goal of our research is that if people are victims of fraud when buying a product in the current market, they can easily access the laws that exist for that case which are included in consumer act. The types of cases that fall under this category are expired products, products sold at a price higher than the original price, adulterated and counterfeit products, etc. We train the models of this earning model including Naive Bayes, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), LDA, Linear Regression, Logistic Regression, Decision Stump with data collected through the survey (n=1500) according to the type of fraud. After analyzing the models, it was seen that SVM gives the most effective results which showed accuracy of 98%, recall (97%), F1-score (97%), precision (97%). This study will help the people of Bangladesh to easily know which law is applicable for that fraud and what measures need to be taken if they are a victim of fraud when buying any product in the local market. By doing this, it will ultimately create awareness among the businessmen in the country, protect the public health of the common people, and eliminate the syndicate system in the local market. This will improve the economic system of Bangladesh on the one hand as well as the development of the country.